The company is a talent management & training software company that helps organizations recruit, train, and manage their people. They handle a variety of requests, including product bug-related queries and account services. The majority of calls are inbound, but they also handle outbound calls for support case triage and follow-up.
Challenge
At the beginning of 2020, the company acquired another organization, which meant it needed to double its support team from 80 to 160 agents. That meant preparing to train new agents on their process and their service offering. But before onboarding all of those agents, they needed to make sure their quality process was helping to identify which agents needed the most help quickly and at scale.
With its previously manual process, the company was consistently seeing coaching drop on their list of support priorities due to support backlogs. This meant agents didn’t know where they needed to improve or how support leads were making decisions with anecdotes instead of data. Ultimately, the company needed to create a more data-driven, fast, and scalable QA process to meet and exceed its customer retention KPIs while delivering a great CSAT.
Solution
More efficient & transparent QA Process
The company needed more transparency into what agents were facing on calls and know whether or not their quality processes were being followed. Observe.AI allowed them to both streamline their process and use AI to better identify opportunities for improved performance.
With Observe.AI, 100% of their voice interactions were automatically analyzed and recorded. They also customized its dashboard to help the team understand how their actions were impacting major KPIs, including dead air, process adherence, and more.
With the onset of the coronavirus pandemic, the company went all-remote. With Observe.AI, they enabled its support leads to not only evaluate call from anywhere, but do it in less time with higher accuracy—something that had been difficult to do with its previous speech analytics and call quality monitoring system.
Sentiment analysis for better customer retention
The company leveraged the AI-driven ‘Moments’ feature in Observe.AI to pinpoint key areas of interest in conversation that should be analyzed on a deeper level, such as moments of strong negative or positive customer sentiment. This enabled the company to know who needed coaching and which top performers should be celebrated.
Through its tonality-based sentiment detection, Observe.AI’s platform identifies patterns like speech volume, word use, speech rate, and more that can be indicative of a good or bad Customer Experience (CX). One of the ways the company leveraged sentiment analysis was by creating a ‘Raving Fans’ moment. This enabled support leads to identify which agents were creating positive CX by surfacing calls where agents demonstrated empathy or where customers used language like, “this made my day!”, for instance.
Examples of how the company uses Moments
- Empathy Moment: Is the agent using empathy on voice calls for an optimal support experience? Does the client feel that they are being taken care of?
- Raving Fans Moment: Which agents are going above and beyond to provide an exceptional customer experience? How are they thriving and succeeding? What learnings can be shared from top performers to help underperforming agents?
Results
- Reduced number of QA analysts with 100% call coverage
- 83% average transcription accuracy
- Created and validated 15 finely tuned customized moments
- Generated hundreds of untapped coaching opportunities
Thanks to the Observe.AI and Talkdesk one-click integration, the company was able to implement the contact center AI solution in just four weeks. With a centralized dashboard that facilitates a full picture of the QA process and agent performance, the company now leads with efficiency, accuracy, and visibility to drive brand loyalty and customer retention.
.png)
.png)
.png)
.png)
.png)
.png)
.png)
